Investigation of Unsafe Construction Site Conditions Using Deep Learning Algorithms Using Unmanned Aerial Vehicles

• Published in: Sensors (Basel, Switzerland) Vol. 24; no. 20; p. 6737
• Main Authors: Kumar, Sourav, Poyyamozhi, Mukilan, Murugesan, Balasubramanian, Rajamanickam, Narayanamoorthi, Alroobaea, Roobaea, Nureldeen, Waleed
• Format: Journal Article
• Language: English
• Published: Switzerland MDPI AG 01.10.2024; MDPI
• Subjects: Accidents; Algorithms; automatic detection; Automation; Building; Compliance; Construction accidents & safety; Construction industry; Construction workers; Data mining; Data processing; Drone aircraft; Drones; Efficiency; image recognition; object detection; Occupational health and safety; Occupational safety; Personal protective equipment; Privacy; Quality control; Quality management; Safety equipment; Safety management; Safety standards; Strikes; Surveillance; tensor flow; Trends; Unmanned Aerial Vehicle; Unmanned aerial vehicles; unsafe site conditions; Weather; Workers' compensation; object detection; automatic detection; unsafe site conditions; tensor flow; image recognition; Unmanned Aerial Vehicle

The rapid adoption of Unmanned Aerial Vehicles (UAVs) in the construction industry has revolutionized safety, surveying, quality monitoring, and maintenance assessment. UAVs are increasingly used to prevent accidents caused by falls from heights or being struck by falling objects by ensuring workers...